Post-training evaluations. Internally built agents. Applications from external builders. Across each, domain experts reviewing outputs can become the limit on progress.
LLM judges can vary. Repeated expert review slows delivery. Verify agent outputs against your SOPs and domain requirements using deterministic, source-backed checks.
Expert review slows each tuning cycle. Build an internal domain benchmark with deterministic, source-backed checks to identify gaps and assess model iterations.
Vendor evals alone don’t establish fit for your workflow. Check agent outputs against your own SOPs and policies with deterministic, source-backed verification.
Turn SOPs and domain knowledge into a source-backed standard. Verify model and agent outputs against it.
Experts govern knowledge. CogniSwitch verifies outputs.
Illustration only. SOP §4.2, the escalation requirement and the finding are examples, not customer data or measured results. Judgement cases still reach experts.
Compare how verification works.
LLM judges infer a verdict. CogniSwitch checks explicit domain requirements.
What changes
LLM-as-a-judge
CogniSwitch
LLM-as-a-judgeModel judgement
CogniSwitchExplicit domain checks
LLM judges interpret a rubric through a model. CogniSwitch checks outputs against explicit requirements grounded in your domain sources.
LLM-as-a-judgeScore and explanation
CogniSwitchRequirement, source and evidence
A judge’s explanation can help interpret its score. CogniSwitch links a finding to the requirement, its authoritative source and the evidence checked, so your team can inspect the basis for the verdict.
LLM-as-a-judgeRecalibrate as judges change
CogniSwitchSame inputs and rules, same verdict
Judge scores can shift as models and prompts change. CogniSwitch repeats the same checks for the same inputs, rules and verification procedure; changes to your standard should be assessed explicitly.
LLM-as-a-judgeReview and resolve disagreements
CogniSwitchGovern knowledge and handle exceptions
Experts define and maintain the verification standard. Routine outputs are checked against it; cases requiring judgement return to experts.
Use judges for subjective quality; verify explicit requirements with CogniSwitch.
SOPBench: verification against an independent benchmark.
SOPBench
100%
Agreement with benchmark verdicts in the reported evaluation scope.
Developed by researchers at UC Santa Barbara and Google DeepMind, SOPBench tests whether AI agents follow standard operating procedures and constraints.
CogniSwitch identified all 242 procedure breaches included in the study and flagged none of the 487 conversations the benchmark judged compliant. Repeated runs returned identical verdicts across 1,138 tested actions.
Each finding identifies the requirement, the evidence checked and where it appeared in the conversation.
Tested insurance workflow. Four replay discrepancies excluded from the headline result; exclusions are documented in the report.
Inspect a published finding +
call 5 appeal_claim → Not permitted
constraint within_appeal_period (never established)
claim_date not established before this call
appeal_period = 30 days
clock.interaction_time = 2025-01-15T12:56:39Z, turn 8
Simulated insurance workflow. The appeal ran before its required precondition was established. Procedure settings came from benchmark task files.
Bring us the model or agent workflow you’re evaluating, representative outputs, and the domain knowledge, SOPs and operating requirements those outputs need to satisfy.
We’ll work with your team to define what needs verifying, identify the authoritative knowledge behind those checks and scope an evaluation against your requirements.
